﻿* Encoding: UTF-8.

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NOTES ON DATA/SYNTAX

Data set is the complete working data set, i.e. it includes all calculated indexes included in the analysis.
Syntax provided below should replicate four tables included in the manuscrip using this datat. Syntax is squentially ordered by table. Also included is the replication syntax for the
factor analysis performed on all 32 items.

At the end we have also included syntax for a range of additional coding/analyses requested by reviewers. These analyses were not reported in the published manuscript, but we are reporting them as 
we conducted them.



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##DESCRIPTIVES: TABLE 1: VARIABLES AND MEASURES

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DESCRIPTIVE VARIABLES = Compulsion_Battery Social_Lifestyle_8_item Health_6_item
    Emotional_8_item political_malady_additive Factor_32_Item White    black hispanic asian faminc    age   educ Not_employed male agreeable  emotstab dogmatism   pid7   ideo3   social_wp  econ_wp  app_dtrmp other_guy_index   
 polknowscore  newsint  s5_4 s5_6   political_participation  pew_churatd age
  /STATISTICS = MEAN STDDEV MIN MAX

###NOTE: BIRTH YEAR WAS RECORDED ON THE ORIGINAL SURVEY RATHER THAN AGE. THIS IS HOW WE TRANSFORMED BIRTHYEAR INTO AGE:

compute age = 2017 - birthyr.
execute.



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TABLE 2 REPLICATION: THE COSTS OF POLITICS

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###TABLE 2 -- these are the frequencies (percent agreeing) in Table 2

FREQUENCIES VARIABLES=problems_immediate_family problems_extended_family problems_atwork
    damaged_friendship homelife_less_pleasant thinkofpolitics_morethanlike politics_delayed_task
    political_websites politics_lost_temper depressed_cand_loses time_on_politics
    regret_comments politics_focus oonversation_restrained
    politics_bad_judgement politics_health politics_fatigued politics_suicidal politics_stressed
    politics_legal_problems politics_financial politics_workschool politics_sleep
    politics_write_online politics_bad_wishes politics_winloss politics_hate politics_guilty
    politics_media politics_move politics_void politics_annoyed
  /ORDER=ANALYSIS.


##################################

####10 item compulsion battery: Table 2

#################################

## this is just a PCA -- kicks out 1 dimension w/46.6 % of variance

FACTOR
  /VARIABLES S1A_6 S1A_7 S1A_8 S1A_11 S1A_15 S1A_16 S1A_17 S1A_27 S1A_29 S1A_34
  /MISSING LISTWISE
  /ANALYSIS S1A_6 S1A_7 S1A_8 S1A_11 S1A_15 S1A_16 S1A_17 S1A_27 S1A_29 S1A_34
  /PRINT INITIAL EXTRACTION
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION PC
  /ROTATION NOROTATE
  /METHOD=CORRELATION.

##Chronbach's alpha is .871

RELIABILITY
  /VARIABLES=S1A_6 S1A_7 S1A_8 S1A_11 S1A_15 S1A_16 S1A_17 S1A_27 S1A_29 S1A_34
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA.


# Addditive scale

compute Compulsion_Battery = S1A_6 + S1A_7 + S1A_8 + S1A_11 + S1A_15 + S1A_16 + S1A_17 + S1A_27 + S1A_29 + S1A_34.
execute.

CORRELATIONS
  /VARIABLES=Compulsion_Battery S1A_6 S1A_7 S1A_8 S1A_11 S1A_15 S1A_16 S1A_17 S1A_27 S1A_29 S1A_34
      /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.

##################################

###6 item Health Costs Battery: Table 2

##################################

#PCA kicks out one factor -- 57% of variance

FACTOR
  /VARIABLES S1A_10 S1A_18 S1A_19 S1A_20 S1A_21 S1A_26
  /MISSING LISTWISE
  /ANALYSIS S1A_10 S1A_18 S1A_19 S1A_20 S1A_21 S1A_26
  /PRINT INITIAL EXTRACTION
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION PC
  /ROTATION NOROTATE
  /METHOD=CORRELATION.

#chronbach's alpha is .849

RELIABILITY
  /VARIABLES=S1A_10 S1A_18 S1A_19 S1A_20 S1A_21 S1A_26
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA.

#additive scale

compute Health_6_item = S1A_10 + S1A_18 + S1A_19 + S1A_20 + S1A_21 + S1A_26.
execute.

CORRELATIONS
  /VARIABLES=Health_6_item S1A_10 S1A_18 S1A_19 S1A_20 S1A_21 S1A_26
      /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.

###################################

###8 Item Social and Lifestyle Costs Battery: Table 2

##################################

##this is just PCA of social/lifestyle costs kicks out two dimensions, kinda split into family/friendship problems and finalicial/legal/work problems
#but structure matrix shows loadings are high on both dimensions for all items and the two factors are pretty highly correlate (.55). Stil a single dimension arguable

FACTOR
  /VARIABLES S1A_1 S1A_2 S1A_4 S1A_5 S1A_3 S1A_23 S1A_24 S1A_25
  /MISSING LISTWISE
  /ANALYSIS S1A_1 S1A_2 S1A_4 S1A_5 S1A_3 S1A_23 S1A_24 S1A_25
  /PRINT INITIAL EXTRACTION ROTATION
  /FORMAT SORT BLANK(.30)
  /PLOT EIGEN
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION PC
  /CRITERIA ITERATE(25) DELTA(0)
  /ROTATION OBLIMIN
  /METHOD=CORRELATION.

#chronbach's alpha is .856

RELIABILITY
  /VARIABLES=S1A_1 S1A_2 S1A_4 S1A_5 S1A_3 S1A_23 S1A_24 S1A_25
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA.

#addtive scale

compute Social_Lifestyle_8_item = S1A_1 + S1A_2 + S1A_4 + S1A_5 + S1A_3 + S1A_23 + S1A_24 + S1A_25.
execute.

CORRELATIONS
  /VARIABLES=Social_Lifestyle_8_item S1A_1 S1A_2 S1A_4 S1A_5 S1A_3 S1A_23 S1A_24 S1A_25
      /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.

######################################
##8 Item Emotional Costs Battery: Table 2

#####################################

#PCA kicks out single dimension 46% of variance

FACTOR
  /VARIABLES S1A_9 S1A_13 S1A_28 S1A_30 S1A_31 S1A_32 S1A_33 S1A_35
  /MISSING LISTWISE
  /ANALYSIS S1A_9 S1A_13 S1A_28 S1A_30 S1A_31 S1A_32 S1A_33 S1A_35
  /PRINT INITIAL EXTRACTION
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION PC
  /ROTATION NOROTATE
  /METHOD=CORRELATION.


#chronbach's alpha is .833

RELIABILITY
  /VARIABLES=S1A_9 S1A_13 S1A_28 S1A_30 S1A_31 S1A_32 S1A_33 S1A_35
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA.

#additive scale

compute Emotional_8_item = S1A_9 + S1A_13 + S1A_28 + S1A_30 + S1A_31 + S1A_32 + S1A_33 + S1A_35.
execute.

CORRELATIONS
  /VARIABLES=Emotional_8_item S1A_9 S1A_13 S1A_28 S1A_30 S1A_31 S1A_32 S1A_33 S1A_35
      /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.


########################################################################################################################################################

##########TEN ITEM BATTERY OF POLITICLA COSTS -- TABLE 3

######################################################################################################################################################


#the analysis below is of the ten items with the higest levels of agreement, i.e. they represent the 10 most frequently reported political health costs of the  32 items.This kicks out a single dimension -- there is not
##really a trace of a second factor. Picks up about 41.5 perct of variance. 


FACTOR
  /VARIABLES  S1A_21 S1A_32 S1A_9 S1A_30 S1A_10  S1A_6 S1A_33 S1A_29 S1A_19 S1A_4
  /MISSING LISTWISE
  /ANALYSIS S1A_21 S1A_32 S1A_9 S1A_30 S1A_10  S1A_6 S1A_33 S1A_29 S1A_19 S1A_4
  /PRINT INITIAL EXTRACTION ROTATION
  /FORMAT SORT BLANK(.30)
  /PLOT EIGEN
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION ML
  /CRITERIA ITERATE(25)
  /ROTATION PROMAX(4)
  /SAVE REG(ALL).



##just chronbach's alpha of the ten items  -- alpha is .88

RELIABILITY
  /VARIABLES=S1A_21 S1A_32 S1A_9 S1A_30 S1A_10  S1A_6 S1A_33 S1A_29 S1A_19 S1A_4
  /SCALE('ALL VARIABLES') ALL
  /MODEL=ALPHA.

##additive scale of these ten items--theoretical scale of 5 to 50, where 50 indicates politics having bigger negative imapct on life

compute political_malady_additive  = S1A_21  + S1A_32 + S1A_9 + S1A_30 + S1A_10 + S1A_6 + S1A_33 + S1A_29 + S1A_19 + S1A_4 .
execute.

##just correlations of three single dimenion scales -- correlations are .87 to .99. Clearly picking up same thing

CORRELATIONS
  /VARIABLES=political_malady_additive  S1A_21 S1A_32 S1A_9 S1A_30 S1A_10 S1A_6 S1A_33 S1A_29 S1A_19 S1A_4 
  /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.


DESCRIPTIVES VARIABLES = political_malady_additive
     /STATISTICS = MEAN STDDEV MIN MAX.



###############################################################################################################

##32 ITEM FACTOR ANALYSIS--

####################################################################################################################

FACTOR
  /VARIABLES S1A_1 S1A_2 S1A_3 S1A_4 S1A_5 S1A_6 S1A_7 S1A_8 S1A_9 S1A_10 S1A_11 S1A_13 S1A_15
    S1A_16 S1A_17 S1A_18 S1A_19 S1A_20 S1A_21 S1A_23 S1A_24 S1A_25 S1A_26 S1A_27 S1A_28 S1A_29 S1A_30
    S1A_31 S1A_32 S1A_33 S1A_34 S1A_35
  /MISSING LISTWISE
  /ANALYSIS S1A_1 S1A_2 S1A_3 S1A_4 S1A_5 S1A_6 S1A_7 S1A_8 S1A_9 S1A_10 S1A_11 S1A_13 S1A_15
    S1A_16 S1A_17 S1A_18 S1A_19 S1A_20 S1A_21 S1A_23 S1A_24 S1A_25 S1A_26 S1A_27 S1A_28 S1A_29 S1A_30
    S1A_31 S1A_32 S1A_33 S1A_34 S1A_35
  /PRINT INITIAL EXTRACTION ROTATION
  /FORMAT SORT BLANK(.30)
  /PLOT EIGEN
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION ML
  /CRITERIA ITERATE(25) DELTA(0)
  /ROTATION OBLIMIN.

## yields 5 factor solution, but scree plot shows it's really no more than 2 or 3 at most -- does not really break down into coherent factors.
## given that, in this syntax below I'm just repeating the factor analysis and forcing a 1 factor solution, save this -- it's variable name
## is "Factor_32_Item" in the data set.This is what we use as the single index from the 32 items

FACTOR
  /VARIABLES S1A_1 S1A_2 S1A_3 S1A_4 S1A_5 S1A_6 S1A_7 S1A_8 S1A_9 S1A_10 S1A_11 S1A_13 S1A_15
    S1A_16 S1A_17 S1A_18 S1A_19 S1A_20 S1A_21 S1A_23 S1A_24 S1A_25 S1A_26 S1A_27 S1A_28 S1A_29 S1A_30
    S1A_31 S1A_32 S1A_33 S1A_34 S1A_35
  /MISSING LISTWISE
  /ANALYSIS S1A_1 S1A_2 S1A_3 S1A_4 S1A_5 S1A_6 S1A_7 S1A_8 S1A_9 S1A_10 S1A_11 S1A_13 S1A_15
    S1A_16 S1A_17 S1A_18 S1A_19 S1A_20 S1A_21 S1A_23 S1A_24 S1A_25 S1A_26 S1A_27 S1A_28 S1A_29 S1A_30
    S1A_31 S1A_32 S1A_33 S1A_34 S1A_35
  /PRINT INITIAL EXTRACTION ROTATION
  /FORMAT SORT BLANK(.30)
  /PLOT EIGEN
  /CRITERIA FACTORS(1) ITERATE(25)
  /EXTRACTION ML
  /CRITERIA ITERATE(25) DELTA(0)
  /ROTATION OBLIMIN
  /SAVE REG(ALL).


##############################################################################################################################



###########################################################################################################################################

############ TABLE 4--JUST BIVARIATE CORRELATIONS WITH NO CONTROLS###############################################

##########################################################################################################################################


CORRELATIONS
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  
white  black    hispanic asian faminc    age  educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.


###################################################################################################################################

##########TABLE 5 REGRESSION MODELS##############################################################################

###################################################################################################################################


###TABLE 5 32 ITEM ALL POL ATT VARIABLES

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT factor_32_item
  /METHOD=ENTER black hispanic asian    faminc    age  educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).

###TABLE 5 10 ITEM ALL POL ATT VARIABLES

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT political_malady_additive
  /METHOD=ENTER black hispanic asian    faminc    age  educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).

## NOTE: Reviewers were concerned about possibility of multicollinearity in the two regression models above. VIFs suggest this isn't a big problem, but wanted us to create a single
## ideology/political beliefs measure (see text). Below is the syntax for a the factor analysis used to create that single measure of political leaning: 


FACTOR
  /VARIABLES pid7    ideo3 social_wp  econ_wp   app_dtrmp
  /MISSING LISTWISE
  /ANALYSIS pid7    ideo3 social_wp  econ_wp   app_dtrmp
  /PRINT INITIAL EXTRACTION ROTATION
  /FORMAT SORT BLANK(.30)
  /PLOT EIGEN
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION ML
  /CRITERIA ITERATE(25) DELTA(0)
  /ROTATION OBLIMIN
  /SAVE REG(ALL).

## this finds one dimenions that picks up 65% of variance


####TABLE 5 32 ITEM USING FACTOR SCORE FOR POL ATT

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT factor_32_item
  /METHOD=ENTER black  hispanic asian   faminc    age  educ Not_employed  male   agreeable emotstab dogmatism FAC1_4  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).

####TABLE 5 USING 10 ITEM USING FACTOR SCORE FOR POL ATT

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT political_malady_additive
  /METHOD=ENTER black  hispanic asian   faminc    age  educ Not_employed  male   agreeable emotstab dogmatism  fac1_4  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).







###################################################################################################################################################################################
###################################################################################################################################################################################

#########ALL SYNTAX BELOW REFLECT ANALYSES AND ALTERNATE CODING REVIEWERS REQUESTED US TO DO -- THIS IS NOT REPORTED IN THE MANUSCIPT, BUT WE ARE REPORTING
#########THESE ANALYSES AS WE CONDUCTED THEM AS PART OF THE PEER REVEW PROCESS



###ADDITIONAL CORRELATIONS AMONG ITEMS


CORRELATIONS
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  pid7 ideo3  social_wp econ_wp  app_dtrmp
  /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   pid7 ideo3  social_wp econ_wp  app_dtrmp   BY   white black    faminc  age  educ Not_employed  male   
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.




#############################REDOING MODELS IN TABLE 5 USING EDUCATION/CHURCH ATTENDANCE AS CATEGORICAL VARIABLES##########################################

##One reviewer was concerned about using church and education in regression models and suggested using categories (e.g. never attend vs go to church)
####Analyses below recode race and education variables into categories and re-runmodels in Table 5

################################################################################################################################

recode pew_churatd (6=1) (else=0) into never_attend.
execute.

recode educ (1=1) (else=0) into no_hs.
execute.

recode educ (5=1) (6=1) (else=0) into college_grad.
execute.


###TABLE 5 10 ITEM ALL POL ATT VARIABLES USING EDUC CHURCH ATT AS CAT VARS--THIE CATEGORIAL VARIABLES FOR CHURCH/ED ARE NOT SIG AND INFERENCES DO NOT CHANGE

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT factor_32_item
  /METHOD=ENTER black hispanic asian    faminc    age  no_hs college_grad Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  never_attend
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).


REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT political_malady_additive
  /METHOD=ENTER black hispanic asian    faminc    age  no_hs college_grad Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  never_attend
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).


REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT factor_32_item
  /METHOD=ENTER black  hispanic asian   faminc    age  no_hs college_grad Not_employed  male   agreeable emotstab dogmatism FAC1_4  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  never_attend
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).

####TABLE 5 USING 10 ITEM USING FACTOR SCORE FOR POL ATT

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT political_malady_additive
  /METHOD=ENTER black  hispanic asian   faminc    age  no_hs college_grad Not_employed  male   agreeable emotstab dogmatism  fac1_4  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  never_attend
  /SCATTERPLOT=(*ZPRED ,*ZRESID)
  /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
  /CASEWISE PLOT(ZRESID) OUTLIERS(3).




###################################################################################################################################################################

##PARTIAL CORRELATIONS: THESE ANALYSES ARE NOT INCLUDED IN THE PUBLISHED MANUSCRIPT -- THEY WERE TAKEN OUT BASED ON REVIEWER FEEDBACK AND REPLACED WITH 
SIMPLE BIVARIATE CORRELATIONS (SEE BELOW  FOR SYNTAX ON TABLE 4). WE'VE INCLUDED THE SYNTAX HERE JUST TO SAVE ANYONE TIME IF THEY NEED THE FULL PARTIALS

######################################################################################################################################################################

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  white  BY   black    faminc    age  educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  black  BY   white   faminc    age  educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   faminc  BY   white black     age  educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   age BY   white black    faminc    educ Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  educ  BY   white black    faminc  age   Not_employed  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.



PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  Not_employed   BY   white black    faminc  age  educ  male   agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   male  BY   white black    faminc  age  educ Not_employed    agreeable emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item    agreeable  BY   white black    faminc  age  educ Not_employed  male  emotstab dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   emotstab  BY   white black    faminc  age  educ Not_employed  male  agreeable  dogmatism  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   dogmatism  BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab  pid7    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   pid7   BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism    ideo3 social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  ideo3   BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   social_wp  econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  social_wp  BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3   econ_wp   app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  econ_wp BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp     app_dtrmp  other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   app_dtrmp BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp     other_guy_index    
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.



PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item other_guy_index      BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp   app_dtrmp   
 polknowscore  newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  polknowscore     BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp   app_dtrmp   other_guy_index
   newsint   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.



PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  newsint     BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp   app_dtrmp   other_guy_index
   polknowscore   s5_4 s5_6   political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.



PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item   s5_6   BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp   app_dtrmp   other_guy_index
   polknowscore newsint s5_4      political_participation  pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item     political_participation    BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp   app_dtrmp   other_guy_index
   polknowscore newsint s5_4 s5_6    pew_churatd 
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.


PARTIAL CORR
  /VARIABLES= Health_6_item  Emotional_8_item Compulsion_Battery Social_Lifestyle_8_item 
    political_malady_additive Factor_32_Item  pew_churatd     BY   white black    faminc  age  educ Not_employed  male  agreeable  emotstab dogmatism   pid7   ideo3  social_wp econ_wp   app_dtrmp   other_guy_index
   polknowscore newsint s5_4 s5_6    political_participation   
  /SIGNIFICANCE=TWOTAIL
  /MISSING=LISTWISE.

